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Near Instance Optimal Model Selection for Pure Exploration Linear Bandits

This repository contains the python code for our AISTATS 2022 paper Near Instance Optimal Model Selection for Pure Exploration Linear Bandits. Packages used include: numpy, sys, multiprocessing, pickle, time, logging and matplotlib.

Let x = 0 (for experiment in Section 7) or x = 1 (for experiment in Appendix F, we also set max_iter = 500 in algs_class.py in this experiment). Use the following commands to reproduce our experiments.

python3 run_algs.py x
python3 plot.py x

On a cluster consists of two Intel® Xeon® Gold 6254 Processors, the runtime for experiment in Section 7 is around 20 minutes and the runtime for experiment in Appendix F is around 4.5 hours.

Part of the code is obtained/adapted from GitHub - fiezt/Transductive-Linear-Bandit-Code. We thank authors of Sequential Experimental Design for Transductive Linear Bandits for their open-source code. We include a copy of their License in folder LICENSE_OTHERS.

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Code for AISTATS 2022 paper - Near Instance Optimal Model Selection for Pure Exploration Linear Bandits

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